Machine Learning Based Edge Placement Error Analysis and Optimization: A Systematic Review

Anh Tuan Ngo, Bappaditya Dey, Sandip Halder, Stefan De Gendt, Changhai Wang

Research output: Contribution to journalArticlepeer-review

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Abstract

As the semiconductor manufacturing process is moving towards the 3 nm node, there is a crucial need to reduce the edge placement error (EPE) to ensure proper functioning of the integrated circuit (IC) devices. EPE is the most important metric that quantify the fidelity of fabricated patterns in multi-patterning processes, and it is the combination of overlay errors and critical dimension (CD) errors. Recent advances in machine learning have enabled many new possibilities to improve the performance and efficiency of EPE optimization techniques. In this paper, we conducted a survey of recent research work that applied machine learning/ deep learning techniques for the purposes of enhancing virtual overlay metrology, reducing overlay error, and improving mask optimization methods for EPE reduction. Thorough discussions about the objectives, datasets, input features, models, key findings, and limitations are provided. In general, the results of the review work show a great potential of machine learning techniques in aiding the improvement of EPE in the field of semiconductor manufacturing.

Original languageEnglish
JournalIEEE Transactions on Semiconductor Manufacturing
DOIs
Publication statusE-pub ahead of print - 26 Oct 2022

Keywords

  • deep learning
  • Edge placement error
  • Machine learning
  • machine learning
  • metrology
  • Metrology
  • optical proximity correction
  • Optical variables measurement
  • Optimization
  • overlay
  • Predictive models
  • semiconductor
  • Semiconductor device measurement
  • Semiconductor device modeling
  • sub-resolution assist feature

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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